es | en | pt | fr
    • Presentación
    • Países
    • Instituciones
    • Participa
        JavaScript is disabled for your browser. Some features of this site may not work without it.
        Ver ítem 
        •   Inicio
        • Colombia
        • Universidades
        • Universidad Jorge Tadeo Lozano (Colombia)
        • Ver ítem
        •   Inicio
        • Colombia
        • Universidades
        • Universidad Jorge Tadeo Lozano (Colombia)
        • Ver ítem

        Time series forecasting of COVID-19 transmission in Canada using LSTM networks

        Registro en:
        0960-0779
        https://doi.org/10.1016/j.chaos.2020.109864
        http://hdl.handle.net/20.500.12010/11115
        https://doi.org/10.1016/j.chaos.2020.109864
        http://repositorioslatinoamericanos.uchile.cl/handle/2250/3493579
        Autor
        Reddy Chimmula, Vinay Kumar
        Zhang, Lei
        Institución
        • Universidad Jorge Tadeo Lozano (Colombia)
        Resumen
        On March 11th 2020, World Health Organization (WHO) declared the 2019 novel corona virus as global pandemic. Corona virus, also known as COVID-19 was first originated in Wuhan, Hubei province in China around December 2019 and spread out all over the world within few weeks. Based on the public datasets provided by John Hopkins university and Canadian health authority, we have developed a forecasting model of COVID-19 outbreak in Canada using state-of-the-art Deep Learning (DL) models. In this novel research, we evaluated the key features to predict the trends and possible stopping time of the current COVID-19 outbreak in Canada and around the world. In this paper we presented the Long short-term memory (LSTM) networks, a deep learning approach to forecast the future COVID-19 cases. Based on the results of our Long short-term memory (LSTM) network, we predicted the possible ending point of this outbreak will be around June 2020. In addition to that, we compared transmission rates of Canada with Italy and USA. Here we also presented the 2, 4, 6, 8, 10, 12 and 14th day predictions for 2 successive days. Our forecasts in this paper is based on the available data until March 31, 2020. To the best of our knowledge, this of the few studies to use LSTM networks to forecast the infectious diseases
        Materias
        Epidemic transmission
        Time series forecasting
        Machine learning
        Corona virus
        COVID-19
        Long short term memory (LSTM) networks

        Mostrar el registro completo del ítem


        Red de Repositorios Latinoamericanos
        + de 8.000.000 publicaciones disponibles
        500 instituciones participantes
        Dirección de Servicios de Información y Bibliotecas (SISIB)
        Universidad de Chile
        Ingreso Administradores
        Colecciones destacadas
        • Tesis latinoamericanas
        • Tesis argentinas
        • Tesis chilenas
        • Tesis peruanas
        Nuevas incorporaciones
        • Argentina
        • Brasil
        • Colombia
        • México
        Dirección de Servicios de Información y Bibliotecas (SISIB)
        Universidad de Chile
        Red de Repositorios Latinoamericanos | 2006-2018
         

        EXPLORAR POR

        Instituciones
        Fecha2011 - 20202001 - 20101951 - 20001901 - 19501800 - 1900

        Explorar en Red de Repositorios

        Países >
        Tipo de documento >
        Fecha de publicación >
        Instituciones >

        Red de Repositorios Latinoamericanos
        + de 8.000.000 publicaciones disponibles
        500 instituciones participantes
        Dirección de Servicios de Información y Bibliotecas (SISIB)
        Universidad de Chile
        Ingreso Administradores
        Colecciones destacadas
        • Tesis latinoamericanas
        • Tesis argentinas
        • Tesis chilenas
        • Tesis peruanas
        Nuevas incorporaciones
        • Argentina
        • Brasil
        • Colombia
        • México
        Dirección de Servicios de Información y Bibliotecas (SISIB)
        Universidad de Chile
        Red de Repositorios Latinoamericanos | 2006-2018